forked from s452662/SystemyRozmyte
added prototype prediction model
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81
data_filters.py
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81
data_filters.py
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import pandas as pd
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from simpful import *
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def generateTrainingData(dataframe):
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columns = ['season','date','home_team','away_team','result_full','home_passes','away_passes',
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'home_possession','away_possession','home_shots','away_shots']
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return dataframe[columns]
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def generateFuzzyLogicData(dataframe):
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columns = ['season','date','home_team','away_team','result_full','c_home_passes','c_away_passes',
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'c_home_possession','c_away_possession','c_home_shots','c_away_shots','c_home_form','c_away_form',
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'c_home_diff', 'c_away_diff']
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return dataframe[columns]
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def last5Matches(season, teamA, data, df):
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# Wybierz rekordy dla danej pary drużyn i sezonu
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subset = df[((df['season'] == season) & ((df['home_team'] == teamA) | (df['away_team'] == teamA)))]
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# Filtruj dane, aby zawierały te przed daną datą
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before_given_date = subset[pd.to_datetime(subset['date']) < pd.to_datetime(data)]
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# Posortuj wg daty w odwrotnej kolejności
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before_given_date = before_given_date.sort_values(by='date', ascending=False)
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# Wybierz 5 ostatnich przed daną datą
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last_before_date = before_given_date.head(5)
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return last_before_date
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def getResult(score,teamHome):
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x,y = score.split('-')
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x = int(x)
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y = int(y)
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if (x > y and teamHome == True) or (x < y and teamHome == False):
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return "win"
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elif x == y:
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return "draw"
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else:
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return "loss"
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def calculatePoints(matches, team):
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points = 0
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for index, row in matches.iterrows():
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if team == row['home_team']:
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teamHome = True
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else:
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teamHome = False
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x = getResult(row['result_full'], teamHome)
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#print(x)
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if x == "win":
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points = points + 3
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elif x == "draw":
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points = points + 1
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if matches.shape[0] != 0:
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points_avg = points / matches.shape[0]
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else:
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points_avg = 0
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return points_avg
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def calculateGoalDifference(matches, team):
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goal_diff = 0
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for index, row in matches.iterrows():
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if team == row['home_team']:
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teamHome = True
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else:
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teamHome = False
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x,y = row['result_full'].split('-')
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x = int(x)
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y = int(y)
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if teamHome:
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goal_diff = goal_diff + (x-y)
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else:
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goal_diff = goal_diff + (y-x)
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return goal_diff
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128
main.py
128
main.py
@ -1,7 +1,14 @@
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import pandas as pd
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from simpful import *
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from rules import *
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from data_filters import *
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score
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from sklearn.preprocessing import LabelEncoder
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from sklearn.metrics import classification_report
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df = pd.read_csv('df_full_premierleague.csv')
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# Ostatnie 5 spotkań
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@ -12,95 +19,64 @@ df = pd.read_csv('df_full_premierleague.csv')
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#Podania ponizej 300-400 słabo powyżej 500 dużo
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def generateTrainingData(dataframe):
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columns = ['season','date','home_team','away_team','result_full','home_passes','away_passes',
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'home_possession','away_possession','home_shots','away_shots']
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return dataframe[columns]
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if __name__ == "__main__":
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df = pd.read_csv('df_full_premierleague.csv')
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def last5Matches(sezon, druzynaA, data, df):
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# Wybierz rekordy dla danej pary drużyn i sezonu
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subset = df[((df['season'] == sezon) & ((df['home_team'] == druzynaA) | (df['away_team'] == druzynaA)))]
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result = last5Matches('10/11', 'Stoke City', '2010-10-02', df)
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#print(result.to_markdown())
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#print(result)
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result = last5Matches('10/11', 'Blackburn Rovers', '2010-10-02', df)
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#print(result.to_markdown())
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#print(result)
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# Filtruj dane, aby zawierały te przed daną datą
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przed_dana_data = subset[pd.to_datetime(subset['date']) < pd.to_datetime(data)]
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print(calculatePoints(result,'Blackburn Rovers'))
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print(calculateGoalDifference(result, 'Blackburn Rovers'))
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# Posortuj wg daty w odwrotnej kolejności
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przed_dana_data = przed_dana_data.sort_values(by='date', ascending=False)
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df = generateTrainingData(df)
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df = add_column(df, categorize_passes, "c_away_passes", "away_passes")
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df = add_column(df, categorize_passes, "c_home_passes", "home_passes")
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# Wybierz 5 ostatnich przed daną datą
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ostatnie_przed_data = przed_dana_data.head(5)
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df = add_column(df, categorize_possesion, "c_away_possession", "away_possession")
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df = add_column(df, categorize_possesion, "c_home_possession", "home_possession")
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return ostatnie_przed_data
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df = add_column(df, categorize_shots, "c_away_shots", "away_shots")
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df = add_column(df, categorize_shots, "c_home_shots", "home_shots")
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print(df.columns)
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df = add_column(df, get_points_home(df), "c_home_form")
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df = add_column(df, get_points_away(df), "c_away_form")
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df = add_column(df, get_diff_home(df), "c_home_diff")
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df = add_column(df, get_diff_away(df), "c_away_diff")
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df = generateFuzzyLogicData(df)
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def getResult(score,teamHome):
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x,y = score.split('-')
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x = int(x)
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y = int(y)
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label_encoder = LabelEncoder()
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df['season'] = label_encoder.fit_transform(df['season'])
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df['c_home_result'] = get_result_list(df,True)
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df['c_away_result'] = get_result_list(df,True)
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temp = df[['home_team', 'away_team']].stack()
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temp[:] = temp.factorize()[0]
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df[['home_team', 'away_team']] = temp.unstack()
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X = df.drop(['result_full', 'date', 'c_home_result', 'c_away_result'], axis=1)
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y = df['c_home_result']
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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if (x > y and teamHome == True) or (x < y and teamHome == False):
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return "win"
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elif x == y:
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return "draw"
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else:
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return "loss"
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model = RandomForestClassifier(n_estimators=100, random_state=42)
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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def calculatePoints(matches, team):
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points = 0
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for index, row in matches.iterrows():
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if team == row['home_team']:
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teamHome = True
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else:
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teamHome = False
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x = getResult(row['result_full'], teamHome)
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print(x)
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if x == "win":
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points = points + 3
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elif x == "draw":
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points = points + 1
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return points
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accuracy = accuracy_score(y_test, y_pred)
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print(f'Dokładność modelu: {accuracy}')
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accuracy = accuracy_score(y_test, y_pred)
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print(f'Dokładność modelu: {accuracy}')
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print(classification_report(y_test, y_pred))
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def calculateGoalDifference(matches, team):
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goal_diff = 0
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for index, row in matches.iterrows():
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if team == row['home_team']:
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teamHome = True
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else:
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teamHome = False
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x,y = row['result_full'].split('-')
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x = int(x)
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y = int(y)
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if teamHome:
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goal_diff = goal_diff + (x-y)
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else:
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goal_diff = goal_diff + (y-x)
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return goal_diff
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def categorize_passes(pass_count):
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if pass_count < 400:
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return 0 #słabo
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elif 400 <= pass_count <= 500:
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return 1 #średnio
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else:
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return 2 #dużo
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wynik = last5Matches('10/11', 'Stoke City', '2010-10-02', df)
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#print(wynik.to_markdown())
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print(wynik)
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#wynik = last5Matches('10/11', 'Blackburn Rovers', '2010-10-02', df)
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#print(wynik.to_markdown())
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#print(wynik)
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print(calculatePoints(wynik,'Stoke City'))
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print(calculateGoalDifference(wynik, 'Stoke City'))
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df = generateTrainingData(df)
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print(df)
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result = last5Matches('10/11', 'Manchester United', '2010-12-16', df)
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print(calculatePoints(result,'Manchester United'))
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print(calculateGoalDifference(result, 'Manchester United'))
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108
rules.py
108
rules.py
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import simpful
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from data_filters import *
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import pandas as pd
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'''
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def kategoryzuj_strzaly(ilosc_strzalow):
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FS = FuzzySystem()
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TLV = AutoTriangle(3, terms=['mało', 'średnio', 'dużo'], universe_of_discourse=[0, 25])
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ostatnie_spotkania['cat_shots'] = shots
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return ostatnie_spotkania
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'''
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def categorize_shots(shots):
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if shots >= 12:
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return 2
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elif shots <= 6:
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return 0
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else:
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return 1
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def categorize_passes(pass_count):
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if pass_count < 400:
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return 0 #słabo
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elif 400 <= pass_count <= 500:
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return 1 #średnio
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else:
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return 2 #dużo
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def categorize_possesion(shots):
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if shots >= 56:
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return 2
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elif shots <= 40:
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return 0
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else:
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return 1
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def categorize_points(data, row, teamHome):
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if teamHome:
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data_5 = last5Matches(row['season'], row['home_team'], row['date'], data)
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points = calculatePoints(data_5,row['home_team'])
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else:
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data_5 = last5Matches(row['season'], row['away_team'], row['date'], data)
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points = calculatePoints(data_5,row['away_team'])
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if points <=1:
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return 0
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elif points >=2:
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return 2
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else:
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return 1
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def get_points_home(data):
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points = []
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for index, row in data.iterrows():
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points.append(categorize_points(data, row, True))
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return points
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def get_points_away(data):
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points = []
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for index, row in data.iterrows():
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points.append(categorize_points(data, row, False))
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return points
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def categorize_diff(data, row, teamHome):
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if teamHome:
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data_5 = last5Matches(row['season'], row['home_team'], row['date'], data)
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diff = calculateGoalDifference(data_5,row['home_team'])
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else:
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data_5 = last5Matches(row['season'], row['away_team'], row['date'], data)
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diff = calculateGoalDifference(data_5,row['away_team'])
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if diff <=0:
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return 0
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else:
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return 1
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def get_diff_home(data):
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points = []
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for index, row in data.iterrows():
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points.append(categorize_diff(data, row, True))
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return points
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def get_diff_away(data):
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points = []
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for index, row in data.iterrows():
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points.append(categorize_diff(data, row, False))
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return points
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def add_column(data_frame, transform_function, new_column, existing_column=None):
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if existing_column != None:
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new_column_values = data_frame[existing_column].apply(transform_function)
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data_frame[new_column] = new_column_values
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else:
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new_column_values = transform_function
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data_frame[new_column] = new_column_values
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return data_frame
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def get_result_list(df, home_team):
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results = []
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for score in df['result_full']:
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results.append(getResult(score,home_team))
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return results
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